Prediction of Lignin Content of Manchurian Walnut by BP Neural Network and Near-Infrared Spectroscopy

2011 ◽  
Vol 267 ◽  
pp. 991-994
Author(s):  
Zhi Hua Qu ◽  
Li Hai Wang
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Lignin Content ◽  
Model Performance ◽  
Bp Network ◽  
Wood Processing ◽  
Main Component

The lignin as a main component of wood, its content is an important chemical property of wood materials, it has an great effect on the other properties of wood and wood processing and utilization property. In paper making industry, the lignin content is a basis for developing pulp cooking and bleaching process. With the advantages of simple structure, plasticity and obviously superiority in nonlinear data processing, BP neural network and NIR for Manchurian Walnut wood lignin content prediction was investigated in this paper. The original spectra were collected and pretreated with the first derivative. Thriteen typical wave lengths were selected as BP network inputs to establish prediction model for wood lignin content. Model was validated using cross-validation approach. The prediction correlation coefficient (R) is 0.9233 while the root mean square error of prediction (RMSEP) is 0.0179. The results showed that using BP neural network in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood lignin content.

Use of BP Neural Network in Near-Infrared Spectroscopy Calibrations for Predicting of Wood Density

2010 ◽  
Vol 129-131 ◽  
pp. 306-311 ◽  
Author(s):  
Pai Li ◽  
Hong Fu Zhang ◽  
Yao Xiang Li ◽  
Ya Zhao Zhang ◽  
Hui Juan Zhang
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Wood Density ◽  
Near Infrared Spectroscopy ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Cross Validation ◽  
Model Performance ◽  
Bp Network ◽  
Density Prediction

Application of BP neural network and NIRS for larch wood density prediction was investigated in this paper. The original spectra were collected and pretreated with the first derivative and 9 point smoothing. Eleven typical wave lengths were selected as BP network inputs to establish prediction model for wood density. Model was validated using cross-validation approach. The prediction correlation coefficient (R) is 0.916 while the root mean square error of prediction (RMSEP) is 0.0221. The results showed that using BP neural network in near-infrared spectroscopy calibration could significantly improve the model performance in order to rapidly and accurately predict wood density.

scholarly journals Deep belief network-based drug identification using near infrared spectroscopy

2017 ◽  
Vol 10 (02) ◽  
pp. 1630011 ◽  
Author(s):  
Huihua Yang ◽  
Baichao Hu ◽  
Xipeng Pan ◽  
Shengke Yan ◽  
Yanchun Feng ◽  
...  
Keyword(s):  
Neural Network ◽  
Infrared Spectroscopy ◽  
Near Infrared Spectroscopy ◽  
Bp Neural Network ◽  
Classification Accuracy ◽  
Near Infrared ◽  
Deep Belief Network ◽  
Support Vector ◽  
Belief Network ◽  
Drug Identification

Near infrared spectroscopy (NIRS) analysis technology, combined with chemometrics, can be effectively used in quick and nondestructive analysis of quality and category. In this paper, an effective drug identification method by using deep belief network (DBN) with dropout mechanism (dropout-DBN) to model NIRS is introduced, in which dropout is employed to overcome the overfitting problem coming from the small sample. This paper tests proposed method under datasets of different sizes with the example of near infrared diffuse reflectance spectroscopy of erythromycin ethylsuccinate drugs and other drugs, aluminum and nonaluminum packaged. Meanwhile, it gives experiments to compare the proposed method’s performance with back propagation (BP) neural network, support vector machines (SVMs) and sparse denoising auto-encoder (SDAE). The results show that for both binary classification and multi-classification, dropout mechanism can improve the classification accuracy, and dropout-DBN can achieve best classification accuracy in almost all cases. SDAE is similar to dropout-DBN in the aspects of classification accuracy and algorithm stability, which are higher than that of BP neural network and SVM methods. In terms of training time, dropout-DBN model is superior to SDAE model, but inferior to BP neural network and SVM methods. Therefore, dropout-DBN can be used as a modeling tool with effective binary and multi-class classification performance on a spectrum sample set of small size.

Qualitative identification of waste textiles based on near-infrared spectroscopy and the back propagation artificial neural network

2021 ◽  
pp. 004051752110075
Author(s):  
Wenxia Li ◽  
Zihan Wei ◽  
Zhengdong Liu ◽  
Yujun Du ◽  
Jiahui Zheng ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Infrared Spectroscopy ◽  
Infrared Spectrum ◽  
Near Infrared Spectroscopy ◽  
Near Infrared ◽  
Back Propagation ◽  
Bp Network ◽  
Artificial Neural ◽  
Near Infrared Spectrum

Hand sorting for different types of waste textiles is time-consuming, laborious and inaccurate. The non-destructive and efficient identification of fibers in waste fabrics is of great significance to the reuse of textile materials. In this paper, 593 samples were selected as the research objects, including polyester, cotton, wool, viscose, nylon, silk, acrylic, polyester/nylon, polyester/cotton, polyester/wool and silk/cotton waste textiles. The near-infrared spectrum of each sample was obtained by a portable near-infrared spectrometer, and the influence of environmental humidity and fabric thickness on the near-infrared spectrum of the sample was discussed to obtain the best test conditions. On this basis, the back propagation artificial neural network (BP-ANN) was applied to the qualitative classification of waste textiles to complete the automatic identification of fabric components in the sorting process. Firstly, a standard sample set was established by waveform clipping and normalization, and a BP-ANN deep web suitable for near-infrared spectroscopy was established. Then the BP network was trained according to the input near-infrared spectrum data of known sample categories and the classification results of the preset 11 types of labels, and the weights and thresholds of each layer were adjusted in the repeated training process. Finally, a 1500 × 100 × 11 network structure was established when the network error was the smallest, and the number of corresponding hidden layer nodes was 100. When the number of training steps was 500, the sum of squared errors reached 0.001, and the model recognition effect was the best. Meanwhile, the validity of the model was verified by inspecting additional 299 samples outside the model, and the recognition accuracy rate of the established model also exceeded 99%, which verified the effectiveness of the model. These results show that this near-infrared qualitative analysis model can more accurately classify and identify waste textiles, especially polyester waste textiles. In addition, it provides a new idea for the recycling and reuse of waste textiles for enterprises.

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